<i>TMEM106B</i> Effect on cognition in Parkinson disease and frontotemporal dementia
Bibliographic record
Abstract
OBJECTIVE: Common variants near TMEM106B associate with risk of developing frontotemporal dementia (FTD). Emerging evidence suggests a role for TMEM106B in neurodegenerative processes beyond FTD. We evaluate the effect of TMEM106B genotype on cognitive decline across multiple neurogenerative diseases. METHODS: We longitudinally followed 870 subjects with diagnoses of Parkinson disease (PD; n = 179), FTD (n = 179), Alzheimer disease (AD; n = 300), memory-predominant mild cognitive impairment (MCI; n = 75), or neurologically normal control subjects (NC; n = 137) at the University of Pennsylvania (UPenn). All participants had annual Mini-Mental State Examination (MMSE; median follow-up duration = 3.0 years) and were genotyped at TMEM106B index single nucleotide polymorphism rs1990622. Genotype effects on cognition were confirmed by extending analyses to additional cognitive instruments (Mattis Dementia Rating Scale-2 [DRS-2] and Montreal Cognitive Assessment [MoCA]) and to an international validation cohort (Parkinson's Progression Markers Initiative [PPMI], N = 371). RESULTS: carriers from the UPenn cohort demonstrated more rapid longitudinal decline in DRS-2 scores. Finally, in the PPMI cohort, TMEM106B risk allele carriers demonstrated more rapid longitudinal decline in MoCA scores. INTERPRETATION: Irrespective of cognitive instrument or cohort assessed, TMEM106B acts as a genetic modifier for cognitive trajectory in PD. Our results implicate lysosomal dysfunction in the pathogenesis of cognitive decline in 2 different proteinopathies. ANN NEUROL 2019;85:801-811.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".